Group recommender systems: Aggregation, satisfaction and group attributes

Abstract

This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modeling the user’s affective state. It summarizes results from previous research in these areas. It explores how group attributes can be incorporated in aggregation strategies. Additionally, it shows how group recommendation techniques can be applied when recommending to individuals, in particular for solving the cold-start problem and dealing with multiple criteria.